Kester Weather Station

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version: 1.0.0

This page displays the measurements taken from our personal weather station. The raw measurements are taken every 15 minutes but we have aggregated the information when pulling it from our PostgreSQL database to every hour, day, and month.

Much improvement is required to make meaningful conclusions from the data and for them to make sense in the context of what the measurements mean.

Background information on our Weather Station project can be found at this github page: Kester Weather Station

Background information on this site and how we manage and display the data generated from the above project can be found at this site: Kester Weather Visualization Site

Charting

Select a time aggregation level and measure on the left to view the corresponding data in chart and table form on the right. The “As Measured” level does not provide any aggregation and shows every measurement taken at 15 minute intervals.

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version: 0.3.2

NOTE: The live version of this website is available here

We are trying to improve our charting work with Observable JS.

Update Note: We have fixed the x-axis labels by properly converting to and dealing with date objects.

This page displays the measurements taken from our personal weather station. The raw measurements are taken every 15 minutes but we have aggregated the information when pulling it from our PostgreSQL database to every hour, day, and month.

Much improvement is required to make meaningful conclusions from the data and for them to make sense in the context of what the measurements mean.

Charting

>>>>>>> feat: Link to the live data website from the index page. Clean out old basic cloud function integrations.

Show the code

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viewof time_aggregation = Inputs.select([
"As Measured",
"hour",
"day",
"month"],
{label: "Time Aggregation"})

viewof measure_type = Inputs.select([
"Air Humidity",
"Air Temperature",
"Barometric Pressure",
"Light Intensity",
"Rain Gauge",
"UV Index",
"Wind Direction Sensor",
"Wind Speed"], 
{label: "Select a measure"})

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viewof time_aggregation = Inputs.select([
"As Measured",
"hour",
"day",
"month"],
{label: "Time Aggregation"})

viewof measure_type = Inputs.select([
"Air Humidity",
"Air Temperature",
"Barometric Pressure",
"Light Intensity",
"Rain Gauge",
"UV Index",
"Wind Direction Sensor",
"Wind Speed"], 
{label: "Select a measure"})

>>>>>>> feat: Link to the live data website from the index page. Clean out old basic cloud function integrations.

Plot

Show the code

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Plot.dot(filteredData,
  {
    x: "date",
    y: "measure", 
    stroke: "measure",
    tip: true
  })
  .plot()

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Plot.dot(filteredData,
  {
    x: "date",
    y: "measure", 
    stroke: "measure",
    tip: true
  })
  .plot()

>>>>>>> feat: Link to the live data website from the index page. Clean out old basic cloud function integrations.

The plot as a line graph.

Show the code

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Plot.plot({
  marks: [
    Plot.line(filteredData, 
      {
        x: "date",
        y: "measure"
      })
    ]
})

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Plot.plot({
  marks: [
    Plot.line(filteredData, 
      {
        x: "date",
        y: "measure"
      })
    ]
})

>>>>>>> feat: Link to the live data website from the index page. Clean out old basic cloud function integrations.

OJS Table

Filtered Data Table

Show the code

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Inputs.table(filteredData)

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Inputs.table(filteredData)

>>>>>>> feat: Link to the live data website from the index page. Clean out old basic cloud function integrations.

Appendix

Find the source code on GitHub at Kester Weather Visualization Site

Show the code

# This function builds the PostgreSQL query required to aggregate the timeseries
#  data to a specified level.
agg_query <- function(agg_function, type, agg_level){
  
  if(agg_level == 'As Measured'){

    query <- sprintf("SELECT time AS date, 
                             type, 
                             \"measurementValue\" AS measure, 
                             \"measurementValue\" AS measure_min,
                             \"measurementValue\" AS measure_max 
                      FROM sensor_data 
                      WHERE type = '%s'",
                      type)
    
  }else{
    
    query <- sprintf("SELECT date, 
                             type, 
                             %s(\"measurementValue\") AS measure,
                             min(\"measurementValue\") AS measure_min,
                             max(\"measurementValue\") AS measure_max 
                      FROM (
                      SELECT date_trunc('%s',time) AS date,
                             type,
                             \"measurementValue\" 
                      FROM sensor_data
                      WHERE type = '%s') AS A 
                      GROUP BY date,
                               type",
            agg_function,
            agg_level,
            type)
    
  }

  return(query)
  
}

measure_gather <- function(con, measure_spec, measure_tib, agg_levels){
  
  for(measure in 1:nrow(measure_spec)){
    
    for(agg in agg_levels){
      
      if(agg == 'As Measured'){
        
        temp <- DBI::dbGetQuery(conn = con,
                                statement = agg_query(agg_function = measure_spec[measure,1],
                                                      type = measure_spec[measure,2],
                                                      agg_level = agg))
        
        measure_tib <- dplyr::bind_rows(measure_tib,
                                        tibble::tibble(aggregate_level = agg,
                                                       temp))
        
      }else{
        
        temp <- DBI::dbGetQuery(conn = con,
                                statement = agg_query(agg_function = measure_spec[measure,1],
                                                      type = measure_spec[measure,2],
                                                      agg_level = agg))
        
        measure_tib <- dplyr::bind_rows(measure_tib,
                                        tibble::tibble(aggregate_level = agg,
                                                       temp))
        
      }
      
    }
    
  }
  
  return(measure_tib)
  
}

Show the code

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if(update){
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if(any(c(!local,update))){
>>>>>>> feat: Link to the live data website from the index page. Clean out old basic cloud function integrations.
  
  load(file = "./connectionInfo.RData")
  
  measure_spec <- tibble::tibble(fun = c('avg',
                                         'avg',
                                         'avg',
                                         'avg',
                                         'sum',
                                         'avg',
                                         'avg',
                                         'avg'),
                                 type = c('Air Humidity',
                                          'Air Temperature',
                                          'Barometric Pressure',
                                          'Light Intensity',
                                          'Rain Gauge',
                                          'UV Index',
                                          'Wind Direction Sensor',
                                          'Wind Speed'))
  
  agg_levels <- c('As Measured','month','day','hour')
  
  measure_tib <- tibble::tibble(aggregate_level = "NA",
                                date = Sys.time(),
                                type = "NA",
                                measure = 1.1,
                                measure_min = 1.1,
                                measure_max = 1.1)[-1,]
  
  con <- DBI::dbConnect(drv = RPostgreSQL::PostgreSQL(),
                        dbname = db,
                        host = host,
                        port = port,
                        user = user,
                        password = password)
  
  measure_tib <- measure_gather(con = con,
                                measure_spec = measure_spec,
                                measure_tib = measure_tib,
                                agg_levels = agg_levels)
  
  DBI::dbDisconnect(conn = con)
  
  save(measure_tib,
       file = "./weatherData.RDS")
  
  rm(con,db,host,password,port,user)
}

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if(file.exists("./weatherData.RDS")){
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if(all(local,file.exists("./weatherData.RDS"))){
>>>>>>> feat: Link to the live data website from the index page. Clean out old basic cloud function integrations.
  
  load(file = "./weatherData.RDS")
  
}

if(exists(x = "measure_tib")){
  
  ojs_define(measures = measure_tib)
  
}

Show the code

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import { aq, op } from '@uwdata/arquero'
d3 = require("d3@7")
parser = d3.timeParse("%Y-%m-%d %H:%M:%S");

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import { aq, op } from '@uwdata/arquero'
d3 = require("d3@7")
parser = d3.timeParse("%Y-%m-%d %H:%M:%S");

>>>>>>> feat: Link to the live data website from the index page. Clean out old basic cloud function integrations.

Show the code

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measures_trans = aq.from(transpose(measures)).derive({ date: aq.escape(d => parser(d.date)) })

filteredData = measures_trans
  .params({
    m: measure_type,
    t: time_aggregation
  })
  .filter((d,p) => op.includes(d.type, p.m) && op.includes(d.aggregate_level, p.t))

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measures_trans = aq.from(transpose(measures)).derive({ date: aq.escape(d => parser(d.date)) })

filteredData = measures_trans
  .params({
    m: measure_type,
    t: time_aggregation
  })
  .filter((d,p) => op.includes(d.type, p.m) && op.includes(d.aggregate_level, p.t))

>>>>>>> feat: Link to the live data website from the index page. Clean out old basic cloud function integrations.

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